Learning the protein language: Evolution, structure, and function.

ABSTRACT: Language models have recently emerged as a powerful machine-learning approach for distilling information from massive protein sequence databases. From readily available sequence data alone, these models discover evolutionary, structural, and functional organization across protein space. Using language models, we can encode amino-acid sequences into distributed vector representations that capture their structural and functional properties, as well as evaluate the evolutionary fitness of sequence variants. We discuss recent advances in protein language modeling and their applications to downstream protein property prediction problems. We then consider how these models can be enriched with prior biological knowledge and introduce an approach for encoding protein structural knowledge into the

SUBMITTER: Bepler T 

PROVIDER: S-EPMC8238390 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature